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Tanabe Lab
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The lab studies evolutionary computation, an optimization approach, from its foundations.
AboutAbout the lab
Exploring how to use and evaluate evolutionary algorithms
The Tanabe Lab conducts fundamental research on evolutionary computation, a field within artificial intelligence and computational intelligence.
Evolutionary computation is a general term for a class of iterative, stochastic optimization methods, including genetic algorithms and evolution strategies.
Evolutionary computation is one useful approach to black-box optimization problems, where the objective function is not given as a formula.
To broaden the application areas of evolutionary computation, the lab works on realizing methods that are higher-performing and easier to use.
Key terms
- Evolutionary computation
- A general term for a class of iterative, stochastic optimization methods. Examples include genetic algorithms and evolution strategies
- Black-box optimization
- An optimization problem in which the objective function is not given as a formula, and only the function values obtained by trying solutions can be used in the search
- Multi-objective optimization
- Optimization that aims to simultaneously minimize multiple objective functions that trade off against each other
- Fitness landscape analysis
- An analysis that captures numerically the properties of the search space of a target problem, such as multimodality
NewsLab news
A master's student's paper was accepted at PPSN2026, an international conference on evolutionary computation
A master's student's paper was accepted at ACM GECCO2026, an international conference on evolutionary computation
A master's student received the Best Presentation Award at a research meeting of the Japanese Society for Evolutionary Computation
A student's paper was accepted at ACM GECCO2025, an international conference on evolutionary computation
A student's paper was accepted by IEEE Transactions on Evolutionary Computation
ResearchResearch themes
The lab focuses mainly on fundamental research in evolutionary computation. Its interests include black-box optimization, multi-objective optimization, experimental analysis of the behavior of evolutionary algorithms, fitness landscape analysis, automated algorithm selection, and the design of benchmarking methodologies.
*Illustrative imageBlack-box optimization
This theme deals with problems in which the objective function is not given as a formula and only the function value of a given solution can be used in the search. Such problems arise in applications where the quality of a solution is evaluated by simulation, and evolutionary computation is one useful approach to them.
*Illustrative imageEvolutionary multi-objective optimization
This theme extends multi-objective optimization, which simultaneously minimizes objectives that trade off against each other, to evolutionary computation. The lab seeks sets of solutions that serve as candidates for a decision maker to choose from, and works on designing new algorithms and analyzing their behavior and performance indicators.
*Illustrative imagePreference-based evolutionary multi-objective optimization
This is an extension of evolutionary multi-objective optimization that incorporates the wishes of a decision maker (preference information) into the search. In general, approximating a part rather than the whole keeps the search cost lower. The lab also analyzes how to evaluate performance.
*Illustrative imageFitness landscape analysis
This analysis quantifies the properties of the search space of a target problem. Knowing properties such as multimodality makes it possible to design or select an algorithm suited to the problem, and also offers clues to why a given algorithm works well or does not.
MembersFaculty
The members of the Tanabe Lab are faculty, and doctoral, master's, and undergraduate students.As of 2026
- Associate ProfessorRyoji Tanabe
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Murmuration ( agrégation) d'un groupe d'étourneaux sur la sebkha de Sijoumi 3” Skander zarrad / CC BY-SA 4.0 (cropped)
- Black-box optimization: “Reactive Search Optimization Position” Roberto Battiti / CC BY-SA 3.0 (cropped)
- Evolutionary multi-objective optimization: “Evolution of horses limbs” Anastasiya Kurilova / CC BY 4.0 (cropped)
- Preference-based evolutionary multi-objective optimization: “Decision making in a social context” Glascher Lab / CC BY-SA 4.0 (cropped)
- Fitness landscape analysis: “Adaptive landscape” Rhiever&action / CC BY 3.0 (cropped)
- Related lab (Shirakawa Lab): “UPS Power Management Module, racks with network cabling in NERSC data center” Derrick Coetzee from Berkeley, CA, USA / CC0 (cropped)
- Related lab (Optimization, Evolutionary Computation, Machine Learning, and Artificial Intelligence Lab (Kento Uchida)): “Bird Swarm - geograph.org.uk - 4777126” Andy Beecroft / CC BY-SA 2.0 (cropped)
- Related lab (Nakata Lab): “Island population model of an evolutionary algorithm” Studi90 / CC BY-SA 4.0 (cropped)
- Related lab (Fujimoto Lab (System Control Lab)): “Sunderland University Experimental Robot Arm” MichaelShort1975 / CC BY-SA 4.0 (cropped)
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